Results of comparative experiments.

<div><p>In the realm of unmanned aerial vehicles, we proposed an enhanced small target detection algorithm, MGAC-YOLO, to address the challenges of missed detections and low accuracy associated with small target identification. Initially, we designed the MConv (Multi-layer Convolution) m...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Xiujing Li (17117670) (author)
مؤلفون آخرون:	Haifei Zhang (283719) (author), Yiliu Hang (21864881) (author), Hao Chen (5190) (author)
منشور في:	2025
الموضوعات:	Genetics Molecular Biology Biotechnology Cancer Infectious Diseases Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> 50 visdrone2019 dataset demonstrate unmanned aerial vehicles low accuracy associated fusion attention mechanism baseline model yolov8s small target identification feature processing capabilities 95 </ sub global attention mechanism global attention )— 3 %, 6 div >< p capture feature information 4 %, respectively 3 %, information capture small targets detection capabilities thereby reducing thereby enriching thereby augmenting sub xmlns shallower level network ’ missed detections local perspectives leading algorithms layer convolution experimental results detection performance contextualized local backbone network
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Results of comparative experiments.

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